class_docstr: Base ModelInterface for torch and tensorflow models.
class_docstr_tok: ["Base","ModelInterface","for","torch","and","tensorflow","models","."]
docstr: Reshape input into model expected input.
  
  Parameters
  ----------
  x: np.ndarray
      A given input that is shaped.
  shape: Tuple[int...]
      The shape of the input.
  channel_first: boolean, optional
      Indicates of the image dimensions are channel first, or channel last.
      Inferred from the input shape if None.
docstr_tok: ["Reshape","input","into","model","expected","input",".","Parameters","--","--","--","--","--","x",":","np.ndarray","A","given","input","that","is","shaped",".","shape",":","Tuple","[","int","...","]","The","shape","of","the","input",".","channel_first",":","boolean",",","optional","Indicates","of","the","image","dimensions","are","channel","first",",","or","channel","last",".","Inferred","from","the","input","shape","if","None","."]
code: def shape_input(
      self,
      x: np.array,
      shape: Tuple[int, ...],
      channel_first: Optional[bool] = None,
      batched: bool = False,
  ):
      """
      Reshape input into model expected input.
  
      Parameters
      ----------
      x: np.ndarray
          A given input that is shaped.
      shape: Tuple[int...]
          The shape of the input.
      channel_first: boolean, optional
          Indicates of the image dimensions are channel first, or channel last.
          Inferred from the input shape if None.
      """
      raise NotImplementedError
  
file_path: Quantus/quantus/helpers/model/model_interface.py
imports: import warnings
  from importlib import util
  from abc import ABC, abstractmethod
  from typing import Any, Dict, Optional, Tuple, List, Union, Generator, TypeVar, Generic
  import numpy
label_desc: Use image node_id 4 for calling the ModelInterface obj's underlying member method code with example usage: obj.shape_input(x, shape, channel_first, batched) without return types